Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
Botnet forensic analysis helps in understanding the nature of attacks and the modus operandi used by the attackers. Botnet attacks\nare difficult to trace because of their rapid pace, epidemic nature, and smaller size. Machine learning works as a panacea for botnet\nattack related issues. It not only facilitates detection but also helps in prevention from bot attack. The proposed inquisition model\nendeavors improved quality of results by comprehensive botnet detection and forensic analysis. This scenario has been applied in\neight different combinations of ensemble classifier technique to detect botnet evidence. The study is also compared to the\nensemble-based classifiers with the single classifier using different parameters. The results exhibit that the proposed model can\nimprove accuracy over a single classifier....
Motor imagery brain-computer interfaces (BCIs) have demonstrated great potential and attract world-spread attentions. Due to the\nnonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This\nprevents them from going into our realistic life. In this paper, the source subjectâ??s data are explored to perform calibration for\ntarget subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to\nhandle is the distribution shift..............................
Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and\nfor detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of\nschizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative\nanalysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework\nused two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant\nfeatures with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision\nmodels with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the\ngroup level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the\ndiagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study\ndemonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching\nover 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed\nframework can be extended to diagnose other diseases....
Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in\nlabeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface\n(BCI) systems, however. In this paper, a novel active learning method is proposed to minimize the amount of labeled, subjectspecific\nEEG data required for effective classifier training, by combining measures of uncertainty and representativeness within an\nextreme learning machine (ELM).............................
The current research on human-machine interaction interface layout focused on ergonomic analysis, while the research on\naesthetics and aesthetic degree calculation of interface layout was insufficient. In order to objectively evaluate the aesthetic degree\nof interface layout, this paper put forward an aesthetic degree evaluation method of interface design based on Kansei engineering.\nFirstly, the perceptual image structure of interface aesthetic degree was analyzed from the perspective of aesthetic cognition. Six\naesthetic image factors affecting interface aesthetic degree, including proportion, conciseness, order, rhythm, density, and\nequilibrium, were extracted by factor analysis method, and the variance contribution rate of each factor was taken as the weight.\nSecondly, according to the six aesthetic degree indexes, the calculation system of interface aesthetic degree was constructed, and\nthe aesthetic degree value of aesthetic image factor was calculated by the corresponding aesthetic degree evaluation mathematical\nformula. Then, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to analyze the order of\naesthetic degree superiority of design schemes, and the comprehensive aesthetic degree evaluation was carried out. Finally, the\naesthetic degree evaluation of human-machine interaction interface layout of the drillerâ??s console of an AC variable frequency\ndrilling rig was taken as an example to verify that this method was helpful for designers to optimize the design scheme. The\nexperimental results showed that the proposed method was feasible and effective compared with the method of paired comparison\ncommonly used in psychophysics....
Loading....